Overview

Dataset statistics

Number of variables22
Number of observations10000
Missing cells11254
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 MiB
Average record size in memory1.3 KiB

Variable types

Numeric8
Categorical6
Text7
Boolean1

Alerts

currency has constant value "USD"Constant
fee has constant value "False"Constant
bathrooms is highly overall correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
id is highly overall correlated with source and 1 other fieldsHigh correlation
source is highly overall correlated with id and 1 other fieldsHigh correlation
square_feet is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
time is highly overall correlated with id and 1 other fieldsHigh correlation
category is highly imbalanced (99.7%)Imbalance
has_photo is highly imbalanced (64.1%)Imbalance
pets_allowed is highly imbalanced (64.8%)Imbalance
price_type is highly imbalanced (99.8%)Imbalance
source is highly imbalanced (69.2%)Imbalance
amenities has 3549 (35.5%) missing valuesMissing
pets_allowed has 4163 (41.6%) missing valuesMissing
address has 3327 (33.3%) missing valuesMissing
square_feet is highly skewed (γ1 = 22.93534918)Skewed
id has unique valuesUnique
bedrooms has 198 (2.0%) zerosZeros

Reproduction

Analysis started2026-02-14 00:59:37.616914
Analysis finished2026-02-14 00:59:46.567985
Duration8.95 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6233957 × 109
Minimum5.5086541 × 109
Maximum5.6686626 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-14T00:59:46.681758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.5086541 × 109
5-th percentile5.5088195 × 109
Q15.5092485 × 109
median5.6686096 × 109
Q35.6686264 × 109
95-th percentile5.6686385 × 109
Maximum5.6686626 × 109
Range1.6000847 × 108
Interquartile range (IQR)1.5937798 × 108

Descriptive statistics

Standard deviation70210252
Coefficient of variation (CV)0.012485384
Kurtosis-0.97374878
Mean5.6233957 × 109
Median Absolute Deviation (MAD)32895.5
Skewness-1.0048073
Sum5.6233957 × 1013
Variance4.9294795 × 1015
MonotonicityNot monotonic
2026-02-14T00:59:46.789040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56686268951
 
< 0.1%
56645971771
 
< 0.1%
56686268331
 
< 0.1%
56599180741
 
< 0.1%
56686267591
 
< 0.1%
56678916761
 
< 0.1%
56686274261
 
< 0.1%
56686266871
 
< 0.1%
56686102901
 
< 0.1%
56686270231
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
55086540871
< 0.1%
55086541491
< 0.1%
55086544601
< 0.1%
55086546071
< 0.1%
55086546381
< 0.1%
55086554161
< 0.1%
55086554711
< 0.1%
55086557901
< 0.1%
55086577001
< 0.1%
55086577581
< 0.1%
ValueCountFrequency (%)
56686625591
< 0.1%
56686623701
< 0.1%
56686433981
< 0.1%
56686433831
< 0.1%
56686433681
< 0.1%
56686433631
< 0.1%
56686433561
< 0.1%
56686433401
< 0.1%
56686433281
< 0.1%
56686433211
< 0.1%

category
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size693.5 KiB
housing/rent/apartment
9996 
housing/rent/home
 
2
housing/rent/short_term
 
2

Length

Max length23
Median length22
Mean length21.9992
Min length17

Characters and Unicode

Total characters219992
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousing/rent/apartment
2nd rowhousing/rent/apartment
3rd rowhousing/rent/apartment
4th rowhousing/rent/apartment
5th rowhousing/rent/apartment

Common Values

ValueCountFrequency (%)
housing/rent/apartment9996
> 99.9%
housing/rent/home2
 
< 0.1%
housing/rent/short_term2
 
< 0.1%

Length

2026-02-14T00:59:46.881717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T00:59:46.945130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
housing/rent/apartment9996
> 99.9%
housing/rent/home2
 
< 0.1%
housing/rent/short_term2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n29996
13.6%
t29996
13.6%
/20000
9.1%
e20000
9.1%
r20000
9.1%
a19992
9.1%
o10004
 
4.5%
h10004
 
4.5%
s10002
 
4.5%
u10000
 
4.5%
Other values (5)39998
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)219992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n29996
13.6%
t29996
13.6%
/20000
9.1%
e20000
9.1%
r20000
9.1%
a19992
9.1%
o10004
 
4.5%
h10004
 
4.5%
s10002
 
4.5%
u10000
 
4.5%
Other values (5)39998
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)219992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n29996
13.6%
t29996
13.6%
/20000
9.1%
e20000
9.1%
r20000
9.1%
a19992
9.1%
o10004
 
4.5%
h10004
 
4.5%
s10002
 
4.5%
u10000
 
4.5%
Other values (5)39998
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)219992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n29996
13.6%
t29996
13.6%
/20000
9.1%
e20000
9.1%
r20000
9.1%
a19992
9.1%
o10004
 
4.5%
h10004
 
4.5%
s10002
 
4.5%
u10000
 
4.5%
Other values (5)39998
18.2%

title
Text

Distinct9350
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
2026-02-14T00:59:47.117906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length80
Median length71
Mean length32.402
Min length5

Characters and Unicode

Total characters324020
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9145 ?
Unique (%)91.5%

Sample

1st rowStudio apartment 2nd St NE, Uhland Terrace NE, Washington, DC 20002
2nd rowStudio apartment 814 Schutte Road
3rd rowStudio apartment N Scott St, 14th St N, Arlington, VA 22209
4th rowStudio apartment 1717 12th Ave
5th rowStudio apartment Washington Blvd, N Cleveland St, Arlington
ValueCountFrequency (%)
br7544
 
11.9%
one4294
 
6.8%
two2430
 
3.8%
apartment2122
 
3.4%
1404
 
2.2%
in1023
 
1.6%
three1008
 
1.6%
st924
 
1.5%
studio923
 
1.5%
street872
 
1.4%
Other values (9078)40771
64.4%
2026-02-14T00:59:47.559187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53315
 
16.5%
e26283
 
8.1%
t17557
 
5.4%
n17104
 
5.3%
r14469
 
4.5%
a14034
 
4.3%
o13932
 
4.3%
i10662
 
3.3%
B9931
 
3.1%
R9376
 
2.9%
Other values (74)137357
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)324020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
53315
 
16.5%
e26283
 
8.1%
t17557
 
5.4%
n17104
 
5.3%
r14469
 
4.5%
a14034
 
4.3%
o13932
 
4.3%
i10662
 
3.3%
B9931
 
3.1%
R9376
 
2.9%
Other values (74)137357
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)324020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
53315
 
16.5%
e26283
 
8.1%
t17557
 
5.4%
n17104
 
5.3%
r14469
 
4.5%
a14034
 
4.3%
o13932
 
4.3%
i10662
 
3.3%
B9931
 
3.1%
R9376
 
2.9%
Other values (74)137357
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)324020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
53315
 
16.5%
e26283
 
8.1%
t17557
 
5.4%
n17104
 
5.3%
r14469
 
4.5%
a14034
 
4.3%
o13932
 
4.3%
i10662
 
3.3%
B9931
 
3.1%
R9376
 
2.9%
Other values (74)137357
42.4%

body
Text

Distinct9961
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2026-02-14T00:59:47.786206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1024
Median length965
Mean length410.0446
Min length6

Characters and Unicode

Total characters4100446
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9929 ?
Unique (%)99.3%

Sample

1st rowThis unit is located at second St NE, Uhland Terrace NE, Washington, DC 20002, Washington, 20002, DCMonthly rental rates range from $790 - $1090We have studio units available for rent
2nd rowThis unit is located at 814 Schutte Road, Evansville, 47712, INMonthly rental rates range from $425 - $445We have studio - 1 beds units available for rent
3rd rowThis unit is located at N Scott St, 14th St N, Arlington, VA 22209, Arlington, 22209, VAMonthly rental rates range from $1390We have studio units available for rent
4th rowThis unit is located at 1717 12th Ave, Seattle, 98122, WAMonthly rental rates range from $925We have studio units available for rent
5th rowThis unit is located at Washington Blvd, N Cleveland St, Arlington, Arlington, 22201, VAMonthly rental rates range from $880We have studio units available for rent
ValueCountFrequency (%)
and16466
 
2.5%
15212
 
2.3%
for13288
 
2.0%
the11701
 
1.8%
is10489
 
1.6%
from9648
 
1.5%
available8799
 
1.4%
beds8600
 
1.3%
unit8451
 
1.3%
a8354
 
1.3%
Other values (24445)537460
82.9%
2026-02-14T00:59:48.126160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
638471
15.6%
e361423
 
8.8%
a264248
 
6.4%
t261599
 
6.4%
n230362
 
5.6%
o220561
 
5.4%
i214745
 
5.2%
r214731
 
5.2%
s185902
 
4.5%
l144400
 
3.5%
Other values (85)1364004
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4100446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
638471
15.6%
e361423
 
8.8%
a264248
 
6.4%
t261599
 
6.4%
n230362
 
5.6%
o220561
 
5.4%
i214745
 
5.2%
r214731
 
5.2%
s185902
 
4.5%
l144400
 
3.5%
Other values (85)1364004
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4100446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
638471
15.6%
e361423
 
8.8%
a264248
 
6.4%
t261599
 
6.4%
n230362
 
5.6%
o220561
 
5.4%
i214745
 
5.2%
r214731
 
5.2%
s185902
 
4.5%
l144400
 
3.5%
Other values (85)1364004
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4100446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
638471
15.6%
e361423
 
8.8%
a264248
 
6.4%
t261599
 
6.4%
n230362
 
5.6%
o220561
 
5.4%
i214745
 
5.2%
r214731
 
5.2%
s185902
 
4.5%
l144400
 
3.5%
Other values (85)1364004
33.3%

amenities
Text

Missing 

Distinct2254
Distinct (%)34.9%
Missing3549
Missing (%)35.5%
Memory size722.4 KiB
2026-02-14T00:59:48.233495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length184
Median length139
Mean length48.040769
Min length2

Characters and Unicode

Total characters309911
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1479 ?
Unique (%)22.9%

Sample

1st rowDishwasher,Elevator,Patio/Deck,Pool,Storage
2nd rowRefrigerator
3rd rowRefrigerator
4th rowAC,Basketball,Cable or Satellite,Gym,Internet Access,Patio/Deck,Pool,Refrigerator
5th rowAC,Basketball,Cable or Satellite,Gym,Internet Access,Patio/Deck,Pool,Refrigerator
ValueCountFrequency (%)
or1678
 
11.8%
cable1258
 
8.8%
dryer915
 
6.4%
disposal,internet455
 
3.2%
floors357
 
2.5%
satellite,dishwasher,garbage238
 
1.7%
ac,cable236
 
1.7%
parking229
 
1.6%
dishwasher,refrigerator225
 
1.6%
dishwasher,garbage205
 
1.4%
Other values (1688)8442
59.3%
2026-02-14T00:59:48.459199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e31767
 
10.3%
r28392
 
9.2%
a25842
 
8.3%
,24677
 
8.0%
o21096
 
6.8%
i17182
 
5.5%
s15385
 
5.0%
t15153
 
4.9%
l14327
 
4.6%
g10383
 
3.4%
Other values (32)105707
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)309911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e31767
 
10.3%
r28392
 
9.2%
a25842
 
8.3%
,24677
 
8.0%
o21096
 
6.8%
i17182
 
5.5%
s15385
 
5.0%
t15153
 
4.9%
l14327
 
4.6%
g10383
 
3.4%
Other values (32)105707
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)309911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e31767
 
10.3%
r28392
 
9.2%
a25842
 
8.3%
,24677
 
8.0%
o21096
 
6.8%
i17182
 
5.5%
s15385
 
5.0%
t15153
 
4.9%
l14327
 
4.6%
g10383
 
3.4%
Other values (32)105707
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)309911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e31767
 
10.3%
r28392
 
9.2%
a25842
 
8.3%
,24677
 
8.0%
o21096
 
6.8%
i17182
 
5.5%
s15385
 
5.0%
t15153
 
4.9%
l14327
 
4.6%
g10383
 
3.4%
Other values (32)105707
34.1%

bathrooms
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.1%
Missing34
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.3805438
Minimum1
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-14T00:59:48.521545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2.5
Maximum8.5
Range7.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.61540986
Coefficient of variation (CV)0.4457735
Kurtosis7.7941199
Mean1.3805438
Median Absolute Deviation (MAD)0
Skewness2.0513031
Sum13758.5
Variance0.3787293
MonotonicityNot monotonic
2026-02-14T00:59:48.590253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
16639
66.4%
22418
 
24.2%
2.5315
 
3.1%
1.5282
 
2.8%
3174
 
1.7%
3.566
 
0.7%
446
 
0.5%
4.512
 
0.1%
58
 
0.1%
72
 
< 0.1%
Other values (4)4
 
< 0.1%
(Missing)34
 
0.3%
ValueCountFrequency (%)
16639
66.4%
1.5282
 
2.8%
22418
 
24.2%
2.5315
 
3.1%
3174
 
1.7%
3.566
 
0.7%
446
 
0.5%
4.512
 
0.1%
58
 
0.1%
5.51
 
< 0.1%
ValueCountFrequency (%)
8.51
 
< 0.1%
81
 
< 0.1%
72
 
< 0.1%
61
 
< 0.1%
5.51
 
< 0.1%
58
 
0.1%
4.512
 
0.1%
446
 
0.5%
3.566
 
0.7%
3174
1.7%

bedrooms
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)0.1%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.7440208
Minimum0
Maximum9
Zeros198
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-14T00:59:48.655871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94235394
Coefficient of variation (CV)0.54033411
Kurtosis2.0717556
Mean1.7440208
Median Absolute Deviation (MAD)1
Skewness1.1736424
Sum17428
Variance0.88803095
MonotonicityNot monotonic
2026-02-14T00:59:48.732396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
14607
46.1%
23398
34.0%
31276
 
12.8%
4404
 
4.0%
0198
 
2.0%
589
 
0.9%
615
 
0.1%
73
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
(Missing)7
 
0.1%
ValueCountFrequency (%)
0198
 
2.0%
14607
46.1%
23398
34.0%
31276
 
12.8%
4404
 
4.0%
589
 
0.9%
615
 
0.1%
73
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
82
 
< 0.1%
73
 
< 0.1%
615
 
0.1%
589
 
0.9%
4404
 
4.0%
31276
 
12.8%
23398
34.0%
14607
46.1%
0198
 
2.0%

currency
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size507.9 KiB
USD
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD10000
100.0%

Length

2026-02-14T00:59:48.813466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T00:59:48.857623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
usd10000
100.0%

Most occurring characters

ValueCountFrequency (%)
U10000
33.3%
S10000
33.3%
D10000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U10000
33.3%
S10000
33.3%
D10000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U10000
33.3%
S10000
33.3%
D10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U10000
33.3%
S10000
33.3%
D10000
33.3%

fee
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
10000 
ValueCountFrequency (%)
False10000
100.0%
2026-02-14T00:59:48.880270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

has_photo
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size560.0 KiB
Thumbnail
8907 
Yes
909 
No
 
184

Length

Max length9
Median length9
Mean length8.3258
Min length2

Characters and Unicode

Total characters83258
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThumbnail
2nd rowThumbnail
3rd rowThumbnail
4th rowThumbnail
5th rowThumbnail

Common Values

ValueCountFrequency (%)
Thumbnail8907
89.1%
Yes909
 
9.1%
No184
 
1.8%

Length

2026-02-14T00:59:48.932740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T00:59:48.982907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
thumbnail8907
89.1%
yes909
 
9.1%
no184
 
1.8%

Most occurring characters

ValueCountFrequency (%)
T8907
10.7%
h8907
10.7%
u8907
10.7%
m8907
10.7%
b8907
10.7%
n8907
10.7%
a8907
10.7%
i8907
10.7%
l8907
10.7%
Y909
 
1.1%
Other values (4)2186
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)83258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T8907
10.7%
h8907
10.7%
u8907
10.7%
m8907
10.7%
b8907
10.7%
n8907
10.7%
a8907
10.7%
i8907
10.7%
l8907
10.7%
Y909
 
1.1%
Other values (4)2186
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)83258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T8907
10.7%
h8907
10.7%
u8907
10.7%
m8907
10.7%
b8907
10.7%
n8907
10.7%
a8907
10.7%
i8907
10.7%
l8907
10.7%
Y909
 
1.1%
Other values (4)2186
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)83258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T8907
10.7%
h8907
10.7%
u8907
10.7%
m8907
10.7%
b8907
10.7%
n8907
10.7%
a8907
10.7%
i8907
10.7%
l8907
10.7%
Y909
 
1.1%
Other values (4)2186
 
2.6%

pets_allowed
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)0.1%
Missing4163
Missing (%)41.6%
Memory size555.4 KiB
Cats,Dogs
5228 
Cats
 
485
Dogs
 
124

Length

Max length9
Median length9
Mean length8.4783279
Min length4

Characters and Unicode

Total characters49488
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCats,Dogs
2nd rowCats,Dogs
3rd rowCats,Dogs
4th rowCats,Dogs
5th rowCats

Common Values

ValueCountFrequency (%)
Cats,Dogs5228
52.3%
Cats485
 
4.9%
Dogs124
 
1.2%
(Missing)4163
41.6%

Length

2026-02-14T00:59:49.045534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T00:59:49.093150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cats,dogs5228
89.6%
cats485
 
8.3%
dogs124
 
2.1%

Most occurring characters

ValueCountFrequency (%)
s11065
22.4%
C5713
11.5%
a5713
11.5%
t5713
11.5%
D5352
10.8%
o5352
10.8%
g5352
10.8%
,5228
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)49488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s11065
22.4%
C5713
11.5%
a5713
11.5%
t5713
11.5%
D5352
10.8%
o5352
10.8%
g5352
10.8%
,5228
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)49488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s11065
22.4%
C5713
11.5%
a5713
11.5%
t5713
11.5%
D5352
10.8%
o5352
10.8%
g5352
10.8%
,5228
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)49488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s11065
22.4%
C5713
11.5%
a5713
11.5%
t5713
11.5%
D5352
10.8%
o5352
10.8%
g5352
10.8%
,5228
10.6%

price
Real number (ℝ)

Distinct1725
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1486.2775
Minimum200
Maximum52500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-14T00:59:49.164718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile658.8
Q1949
median1270
Q31695
95-th percentile2995
Maximum52500
Range52300
Interquartile range (IQR)746

Descriptive statistics

Standard deviation1076.508
Coefficient of variation (CV)0.7242981
Kurtosis547.19449
Mean1486.2775
Median Absolute Deviation (MAD)364.5
Skewness14.367517
Sum14862775
Variance1158869.4
MonotonicityNot monotonic
2026-02-14T00:59:49.264030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135086
 
0.9%
125081
 
0.8%
85079
 
0.8%
110077
 
0.8%
140074
 
0.7%
75072
 
0.7%
105072
 
0.7%
80071
 
0.7%
145071
 
0.7%
95070
 
0.7%
Other values (1715)9247
92.5%
ValueCountFrequency (%)
2001
< 0.1%
2241
< 0.1%
2751
< 0.1%
2881
< 0.1%
3001
< 0.1%
3251
< 0.1%
3502
< 0.1%
3651
< 0.1%
3691
< 0.1%
3861
< 0.1%
ValueCountFrequency (%)
525001
 
< 0.1%
250001
 
< 0.1%
195001
 
< 0.1%
149501
 
< 0.1%
135001
 
< 0.1%
130001
 
< 0.1%
110003
< 0.1%
106001
 
< 0.1%
95004
< 0.1%
94501
 
< 0.1%
Distinct1726
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size531.4 KiB
2026-02-14T00:59:49.535246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length6
Mean length5.4034
Min length4

Characters and Unicode

Total characters54034
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique721 ?
Unique (%)7.2%

Sample

1st row$790
2nd row$425
3rd row$1,390
4th row$925
5th row$880
ValueCountFrequency (%)
1,35086
 
0.9%
1,25081
 
0.8%
85079
 
0.8%
1,10077
 
0.8%
1,40074
 
0.7%
75072
 
0.7%
1,05072
 
0.7%
80071
 
0.7%
1,45071
 
0.7%
95070
 
0.7%
Other values (1717)9249
92.5%
2026-02-14T00:59:49.872443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
$10000
18.5%
17119
13.2%
,7001
13.0%
06445
11.9%
55795
10.7%
94280
7.9%
23270
 
6.1%
72252
 
4.2%
82074
 
3.8%
42060
 
3.8%
Other values (14)3738
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)54034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
$10000
18.5%
17119
13.2%
,7001
13.0%
06445
11.9%
55795
10.7%
94280
7.9%
23270
 
6.1%
72252
 
4.2%
82074
 
3.8%
42060
 
3.8%
Other values (14)3738
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
$10000
18.5%
17119
13.2%
,7001
13.0%
06445
11.9%
55795
10.7%
94280
7.9%
23270
 
6.1%
72252
 
4.2%
82074
 
3.8%
42060
 
3.8%
Other values (14)3738
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
$10000
18.5%
17119
13.2%
,7001
13.0%
06445
11.9%
55795
10.7%
94280
7.9%
23270
 
6.1%
72252
 
4.2%
82074
 
3.8%
42060
 
3.8%
Other values (14)3738
 
6.9%

price_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size547.0 KiB
Monthly
9998 
Weekly
 
1
Monthly|Weekly
 
1

Length

Max length14
Median length7
Mean length7.0006
Min length6

Characters and Unicode

Total characters70006
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMonthly
2nd rowMonthly
3rd rowMonthly
4th rowMonthly
5th rowMonthly

Common Values

ValueCountFrequency (%)
Monthly9998
> 99.9%
Weekly1
 
< 0.1%
Monthly|Weekly1
 
< 0.1%

Length

2026-02-14T00:59:49.957452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T00:59:50.009991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
monthly9998
> 99.9%
weekly1
 
< 0.1%
monthly|weekly1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
y10001
14.3%
l10001
14.3%
M9999
14.3%
n9999
14.3%
o9999
14.3%
h9999
14.3%
t9999
14.3%
e4
 
< 0.1%
W2
 
< 0.1%
k2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)70006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y10001
14.3%
l10001
14.3%
M9999
14.3%
n9999
14.3%
o9999
14.3%
h9999
14.3%
t9999
14.3%
e4
 
< 0.1%
W2
 
< 0.1%
k2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y10001
14.3%
l10001
14.3%
M9999
14.3%
n9999
14.3%
o9999
14.3%
h9999
14.3%
t9999
14.3%
e4
 
< 0.1%
W2
 
< 0.1%
k2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y10001
14.3%
l10001
14.3%
M9999
14.3%
n9999
14.3%
o9999
14.3%
h9999
14.3%
t9999
14.3%
e4
 
< 0.1%
W2
 
< 0.1%
k2
 
< 0.1%

square_feet
Real number (ℝ)

High correlation  Skewed 

Distinct1738
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean945.8105
Minimum101
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-14T00:59:50.078838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile410
Q1649
median802
Q31100
95-th percentile1900
Maximum40000
Range39899
Interquartile range (IQR)451

Descriptive statistics

Standard deviation655.75574
Coefficient of variation (CV)0.69332677
Kurtosis1269.8354
Mean945.8105
Median Absolute Deviation (MAD)202
Skewness22.935349
Sum9458105
Variance430015.58
MonotonicityNot monotonic
2026-02-14T00:59:50.182377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700189
 
1.9%
800161
 
1.6%
600154
 
1.5%
900148
 
1.5%
750139
 
1.4%
250131
 
1.3%
650131
 
1.3%
1000125
 
1.2%
850122
 
1.2%
1100100
 
1.0%
Other values (1728)8600
86.0%
ValueCountFrequency (%)
1011
< 0.1%
1061
< 0.1%
1071
< 0.1%
1161
< 0.1%
1251
< 0.1%
1301
< 0.1%
1321
< 0.1%
1361
< 0.1%
1381
< 0.1%
1411
< 0.1%
ValueCountFrequency (%)
400001
< 0.1%
113181
< 0.1%
87161
< 0.1%
63001
< 0.1%
59211
< 0.1%
57001
< 0.1%
54071
< 0.1%
51991
< 0.1%
50001
< 0.1%
49701
< 0.1%

address
Text

Missing 

Distinct6658
Distinct (%)99.8%
Missing3327
Missing (%)33.3%
Memory size544.4 KiB
2026-02-14T00:59:50.483472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length76
Median length57
Mean length18.566911
Min length4

Characters and Unicode

Total characters123897
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6646 ?
Unique (%)99.6%

Sample

1st row814 Schutte Rd
2nd row1717 12th Avenue
3rd row350 West 50th St
4th row2432 Penmar Avenue
5th row333 Hyde St
ValueCountFrequency (%)
st1735
 
7.1%
avenue795
 
3.2%
dr574
 
2.3%
n470
 
1.9%
s459
 
1.9%
ave428
 
1.7%
e403
 
1.6%
w371
 
1.5%
rd361
 
1.5%
road348
 
1.4%
Other values (7371)18646
75.8%
2026-02-14T00:59:50.926372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18026
 
14.5%
e7898
 
6.4%
a5694
 
4.6%
15661
 
4.6%
t5545
 
4.5%
r5420
 
4.4%
05076
 
4.1%
n4838
 
3.9%
o4286
 
3.5%
23734
 
3.0%
Other values (62)57719
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)123897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18026
 
14.5%
e7898
 
6.4%
a5694
 
4.6%
15661
 
4.6%
t5545
 
4.5%
r5420
 
4.4%
05076
 
4.1%
n4838
 
3.9%
o4286
 
3.5%
23734
 
3.0%
Other values (62)57719
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)123897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18026
 
14.5%
e7898
 
6.4%
a5694
 
4.6%
15661
 
4.6%
t5545
 
4.5%
r5420
 
4.4%
05076
 
4.1%
n4838
 
3.9%
o4286
 
3.5%
23734
 
3.0%
Other values (62)57719
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)123897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18026
 
14.5%
e7898
 
6.4%
a5694
 
4.6%
15661
 
4.6%
t5545
 
4.5%
r5420
 
4.4%
05076
 
4.1%
n4838
 
3.9%
o4286
 
3.5%
23734
 
3.0%
Other values (62)57719
46.6%
Distinct1574
Distinct (%)15.9%
Missing77
Missing (%)0.8%
Memory size562.4 KiB
2026-02-14T00:59:51.127949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length18
Mean length8.7763781
Min length3

Characters and Unicode

Total characters87088
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique682 ?
Unique (%)6.9%

Sample

1st rowWashington
2nd rowEvansville
3rd rowArlington
4th rowSeattle
5th rowArlington
ValueCountFrequency (%)
austin523
 
4.1%
san400
 
3.1%
city312
 
2.5%
dallas216
 
1.7%
houston186
 
1.5%
antonio182
 
1.4%
los167
 
1.3%
angeles165
 
1.3%
chicago148
 
1.2%
saint135
 
1.1%
Other values (1528)10279
80.9%
2026-02-14T00:59:51.431904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a8077
 
9.3%
n7637
 
8.8%
e7050
 
8.1%
o6816
 
7.8%
i5719
 
6.6%
l5314
 
6.1%
t5044
 
5.8%
s4647
 
5.3%
r4447
 
5.1%
2790
 
3.2%
Other values (42)29547
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)87088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a8077
 
9.3%
n7637
 
8.8%
e7050
 
8.1%
o6816
 
7.8%
i5719
 
6.6%
l5314
 
6.1%
t5044
 
5.8%
s4647
 
5.3%
r4447
 
5.1%
2790
 
3.2%
Other values (42)29547
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)87088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a8077
 
9.3%
n7637
 
8.8%
e7050
 
8.1%
o6816
 
7.8%
i5719
 
6.6%
l5314
 
6.1%
t5044
 
5.8%
s4647
 
5.3%
r4447
 
5.1%
2790
 
3.2%
Other values (42)29547
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)87088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a8077
 
9.3%
n7637
 
8.8%
e7050
 
8.1%
o6816
 
7.8%
i5719
 
6.6%
l5314
 
6.1%
t5044
 
5.8%
s4647
 
5.3%
r4447
 
5.1%
2790
 
3.2%
Other values (42)29547
33.9%

state
Text

Distinct51
Distinct (%)0.5%
Missing77
Missing (%)0.8%
Memory size496.7 KiB
2026-02-14T00:59:51.536472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters19846
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDC
2nd rowIN
3rd rowVA
4th rowWA
5th rowVA
ValueCountFrequency (%)
tx1737
17.5%
ca955
 
9.6%
wa519
 
5.2%
nc438
 
4.4%
md424
 
4.3%
nj383
 
3.9%
ga372
 
3.7%
fl339
 
3.4%
oh321
 
3.2%
co318
 
3.2%
Other values (41)4117
41.5%
2026-02-14T00:59:51.736128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A2928
14.8%
T2034
10.2%
C1966
9.9%
N1867
9.4%
X1737
8.8%
M1259
 
6.3%
O1253
 
6.3%
I1222
 
6.2%
W825
 
4.2%
L743
 
3.7%
Other values (14)4012
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)19846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A2928
14.8%
T2034
10.2%
C1966
9.9%
N1867
9.4%
X1737
8.8%
M1259
 
6.3%
O1253
 
6.3%
I1222
 
6.2%
W825
 
4.2%
L743
 
3.7%
Other values (14)4012
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A2928
14.8%
T2034
10.2%
C1966
9.9%
N1867
9.4%
X1737
8.8%
M1259
 
6.3%
O1253
 
6.3%
I1222
 
6.2%
W825
 
4.2%
L743
 
3.7%
Other values (14)4012
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A2928
14.8%
T2034
10.2%
C1966
9.9%
N1867
9.4%
X1737
8.8%
M1259
 
6.3%
O1253
 
6.3%
I1222
 
6.2%
W825
 
4.2%
L743
 
3.7%
Other values (14)4012
20.2%

latitude
Real number (ℝ)

Distinct2395
Distinct (%)24.0%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean37.695162
Minimum21.3155
Maximum61.594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-14T00:59:51.823048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21.3155
5-th percentile29.48144
Q133.67985
median38.8098
Q341.3498
95-th percentile47.454005
Maximum61.594
Range40.2785
Interquartile range (IQR)7.66995

Descriptive statistics

Standard deviation5.4958509
Coefficient of variation (CV)0.14579725
Kurtosis0.40374383
Mean37.695162
Median Absolute Deviation (MAD)4.1735
Skewness0.28166157
Sum376574.67
Variance30.204377
MonotonicityNot monotonic
2026-02-14T00:59:51.933493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.3054512
 
5.1%
29.7714176
 
1.8%
29.4624176
 
1.8%
41.8625139
 
1.4%
43.0724118
 
1.2%
45.5091109
 
1.1%
37.7599102
 
1.0%
34.0372100
 
1.0%
39.074492
 
0.9%
47.61687
 
0.9%
Other values (2385)8379
83.8%
ValueCountFrequency (%)
21.31556
0.1%
21.3881
 
< 0.1%
21.39913
< 0.1%
21.41991
 
< 0.1%
21.44761
 
< 0.1%
25.38011
 
< 0.1%
25.48672
 
< 0.1%
25.59191
 
< 0.1%
25.60651
 
< 0.1%
25.69111
 
< 0.1%
ValueCountFrequency (%)
61.5944
 
< 0.1%
61.31862
 
< 0.1%
61.17381
 
< 0.1%
61.172335
0.4%
61.15211
 
< 0.1%
60.49231
 
< 0.1%
48.787110
 
0.1%
48.41681
 
< 0.1%
48.26041
 
< 0.1%
48.246939
0.4%

longitude
Real number (ℝ)

Distinct2392
Distinct (%)23.9%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-94.652247
Minimum-158.0221
Maximum-70.1916
Zeros0
Zeros (%)0.0%
Negative9990
Negative (%)99.9%
Memory size78.3 KiB
2026-02-14T00:59:52.031562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-158.0221
5-th percentile-122.3275
Q1-101.3017
median-93.6516
Q3-82.209975
95-th percentile-74.0644
Maximum-70.1916
Range87.8305
Interquartile range (IQR)19.091725

Descriptive statistics

Standard deviation15.759805
Coefficient of variation (CV)-0.16650217
Kurtosis-0.15810871
Mean-94.652247
Median Absolute Deviation (MAD)11.105
Skewness-0.66588273
Sum-945575.95
Variance248.37144
MonotonicityNot monotonic
2026-02-14T00:59:52.151799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-97.7497512
 
5.1%
-95.4343176
 
1.8%
-98.5253176
 
1.8%
-87.6825139
 
1.4%
-89.4003118
 
1.2%
-122.6449109
 
1.1%
-122.4379102
 
1.0%
-118.2972100
 
1.0%
-94.552189
 
0.9%
-122.327587
 
0.9%
Other values (2382)8382
83.8%
ValueCountFrequency (%)
-158.02211
 
< 0.1%
-157.93051
 
< 0.1%
-157.83796
 
0.1%
-157.81171
 
< 0.1%
-157.74523
 
< 0.1%
-151.08061
 
< 0.1%
-149.93011
 
< 0.1%
-149.85081
 
< 0.1%
-149.841435
0.4%
-149.53552
 
< 0.1%
ValueCountFrequency (%)
-70.19161
 
< 0.1%
-70.34611
 
< 0.1%
-70.35591
 
< 0.1%
-70.37061
 
< 0.1%
-70.64143
< 0.1%
-70.78543
< 0.1%
-70.82332
< 0.1%
-70.86471
 
< 0.1%
-70.87351
 
< 0.1%
-70.87363
< 0.1%

source
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size574.8 KiB
RentLingo
6912 
RentDigs.com
2764 
ListedBuy
 
179
RealRentals
 
69
GoSection8
 
31
Other values (7)
 
45

Length

Max length17
Median length9
Mean length9.8464
Min length8

Characters and Unicode

Total characters98464
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowRentLingo
2nd rowRentLingo
3rd rowRentLingo
4th rowRentLingo
5th rowRentLingo

Common Values

ValueCountFrequency (%)
RentLingo6912
69.1%
RentDigs.com2764
 
27.6%
ListedBuy179
 
1.8%
RealRentals69
 
0.7%
GoSection831
 
0.3%
Listanza23
 
0.2%
RENTOCULAR16
 
0.2%
rentbits2
 
< 0.1%
Home Rentals1
 
< 0.1%
Real Estate Agent1
 
< 0.1%
Other values (2)2
 
< 0.1%

Length

2026-02-14T00:59:52.239072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rentlingo6912
69.1%
rentdigs.com2764
 
27.6%
listedbuy179
 
1.8%
realrentals69
 
0.7%
gosection831
 
0.3%
listanza23
 
0.2%
rentocular16
 
0.2%
rentbits2
 
< 0.1%
home1
 
< 0.1%
rentals1
 
< 0.1%
Other values (5)5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n16717
17.0%
e10032
10.2%
t9988
10.1%
i9911
10.1%
R9849
10.0%
o9740
9.9%
g9677
9.8%
L7130
7.2%
s3039
 
3.1%
c2796
 
2.8%
Other values (26)9585
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)98464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n16717
17.0%
e10032
10.2%
t9988
10.1%
i9911
10.1%
R9849
10.0%
o9740
9.9%
g9677
9.8%
L7130
7.2%
s3039
 
3.1%
c2796
 
2.8%
Other values (26)9585
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)98464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n16717
17.0%
e10032
10.2%
t9988
10.1%
i9911
10.1%
R9849
10.0%
o9740
9.9%
g9677
9.8%
L7130
7.2%
s3039
 
3.1%
c2796
 
2.8%
Other values (26)9585
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)98464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n16717
17.0%
e10032
10.2%
t9988
10.1%
i9911
10.1%
R9849
10.0%
o9740
9.9%
g9677
9.8%
L7130
7.2%
s3039
 
3.1%
c2796
 
2.8%
Other values (26)9585
9.7%

time
Real number (ℝ)

High correlation 

Distinct6310
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5748912 × 109
Minimum1.568744 × 109
Maximum1.5773622 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-02-14T00:59:52.321125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.568744 × 109
5-th percentile1.5687551 × 109
Q11.5687809 × 109
median1.5773583 × 109
Q31.5773594 × 109
95-th percentile1.5773602 × 109
Maximum1.5773622 × 109
Range8618210
Interquartile range (IQR)8578514.2

Descriptive statistics

Standard deviation3762395.1
Coefficient of variation (CV)0.0023889874
Kurtosis-0.97824144
Mean1.5748912 × 109
Median Absolute Deviation (MAD)2223.5
Skewness-0.99820222
Sum1.5748912 × 1013
Variance1.4155617 × 1013
MonotonicityNot monotonic
2026-02-14T00:59:52.564307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15773592515
 
0.1%
15770168665
 
0.1%
15773594895
 
0.1%
15773594154
 
< 0.1%
15773594104
 
< 0.1%
15770155394
 
< 0.1%
15773589214
 
< 0.1%
15773601354
 
< 0.1%
15773590004
 
< 0.1%
15770150984
 
< 0.1%
Other values (6300)9957
99.6%
ValueCountFrequency (%)
15687439761
< 0.1%
15687439801
< 0.1%
15687439991
< 0.1%
15687440071
< 0.1%
15687440081
< 0.1%
15687440531
< 0.1%
15687440571
< 0.1%
15687440751
< 0.1%
15687441521
< 0.1%
15687441552
< 0.1%
ValueCountFrequency (%)
15773621861
< 0.1%
15773621711
< 0.1%
15773621411
< 0.1%
15773605681
< 0.1%
15773605671
< 0.1%
15773605652
< 0.1%
15773605641
< 0.1%
15773605631
< 0.1%
15773605622
< 0.1%
15773605611
< 0.1%

Interactions

2026-02-14T00:59:45.121758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:40.117699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.000718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.671423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.240050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.873910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.647881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.284986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:45.194822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:40.263354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.106156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.744558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.319292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.957882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.735232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.360977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:45.267770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:40.419377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.181458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.816168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.394520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.034160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.815455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.434157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:45.332971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:40.490054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.255248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.880929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.496442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.109411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.889660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.505132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:45.404428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:40.570154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.354756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.954549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.563767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.187793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.967244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.601343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:45.479753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:40.711488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.442652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.030255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.645116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.272090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.054487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.736642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:45.553146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:40.831958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.521897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.106004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.727188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.496616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.133694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.955373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:45.657836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:40.929629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:41.594014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.173871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:42.799840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:43.571522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:44.209865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T00:59:45.053999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-14T00:59:52.637601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bathroomsbedroomscategoryhas_photoidlatitudelongitudepets_allowedpriceprice_typesourcesquare_feettime
bathrooms1.0000.7020.0000.056-0.062-0.0500.0370.1220.4150.0000.0540.764-0.062
bedrooms0.7021.0000.0000.077-0.0240.0480.0590.1200.3320.0000.0930.698-0.024
category0.0000.0001.0000.0000.0850.0000.0000.0000.0000.0000.0990.0000.108
has_photo0.0560.0770.0001.0000.4070.1240.1520.0410.0000.0000.4020.0000.406
id-0.062-0.0240.0850.4071.000-0.050-0.2730.077-0.0480.1120.540-0.1071.000
latitude-0.0500.0480.0000.124-0.0501.0000.0700.1340.0720.0330.1030.022-0.051
longitude0.0370.0590.0000.152-0.2730.0701.0000.097-0.0800.0000.1690.113-0.273
pets_allowed0.1220.1200.0000.0410.0770.1340.0971.0000.0110.0000.0840.0000.081
price0.4150.3320.0000.000-0.0480.072-0.0800.0111.0000.0000.0000.446-0.048
price_type0.0000.0000.0000.0000.1120.0330.0000.0000.0001.0000.0660.0000.075
source0.0540.0930.0990.4020.5400.1030.1690.0840.0000.0661.0000.1170.547
square_feet0.7640.6980.0000.000-0.1070.0220.1130.0000.4460.0000.1171.000-0.107
time-0.062-0.0240.1080.4061.000-0.051-0.2730.081-0.0480.0750.547-0.1071.000

Missing values

2026-02-14T00:59:46.064240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-14T00:59:46.250521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-14T00:59:46.454408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idcategorytitlebodyamenitiesbathroomsbedroomscurrencyfeehas_photopets_allowedpriceprice_displayprice_typesquare_feetaddresscitynamestatelatitudelongitudesourcetime
05668626895housing/rent/apartmentStudio apartment 2nd St NE, Uhland Terrace NE, Washington, DC 20002This unit is located at second St NE, Uhland Terrace NE, Washington, DC 20002, Washington, 20002, DCMonthly rental rates range from $790 - $1090We have studio units available for rentNaNNaN0.0USDNoThumbnailNaN790$790Monthly101NaNWashingtonDC38.9057-76.9861RentLingo1577359415
15664597177housing/rent/apartmentStudio apartment 814 Schutte RoadThis unit is located at 814 Schutte Road, Evansville, 47712, INMonthly rental rates range from $425 - $445We have studio - 1 beds units available for rentNaNNaN1.0USDNoThumbnailNaN425$425Monthly106814 Schutte RdEvansvilleIN37.9680-87.6621RentLingo1577017063
25668626833housing/rent/apartmentStudio apartment N Scott St, 14th St N, Arlington, VA 22209This unit is located at N Scott St, 14th St N, Arlington, VA 22209, Arlington, 22209, VAMonthly rental rates range from $1390We have studio units available for rentNaN1.00.0USDNoThumbnailNaN1390$1,390Monthly107NaNArlingtonVA38.8910-77.0816RentLingo1577359410
35659918074housing/rent/apartmentStudio apartment 1717 12th AveThis unit is located at 1717 12th Ave, Seattle, 98122, WAMonthly rental rates range from $925We have studio units available for rentNaN1.00.0USDNoThumbnailNaN925$925Monthly1161717 12th AvenueSeattleWA47.6160-122.3275RentLingo1576667743
45668626759housing/rent/apartmentStudio apartment Washington Blvd, N Cleveland St, ArlingtonThis unit is located at Washington Blvd, N Cleveland St, Arlington, Arlington, 22201, VAMonthly rental rates range from $880We have studio units available for rentNaNNaN0.0USDNoThumbnailNaN880$880Monthly125NaNArlingtonVA38.8738-77.1055RentLingo1577359401
55667891676housing/rent/apartment0 BR in New York NY 10019**RARE GEM WITH PRIVATE OUTDOOR TERRACE****AVAILABLE IMMEDIATELY** $2475 RENT IS NET EFFECTIVE WITH one month FREE ON $2700 Monthly rent***Low Fee*UNFURNISHED Spacious and Sunny Southern facing studio (403 sq-ft) with big private terrace (130 sq. feet) basks in natural sunlight all day long, facing the scenic landscaped and peaceful residents ct. yard. The apartment features hard wood floors throughout, a carousel kitchen with new appliances and two big closets. Laundry on floor steps away.Worldwide Plaza is a full-service condo that pampers its residents with concierge and 24-hr attendant at door services, a live-in resident manager, a scenic landscaped court yard and lounge, valet service, 8 laundry rooms (washer and dryers are permitted within the residences), on-site garage and a separate full-service health club (NYSC ELITE) equipped with a 25 meter salt water pool, immersive VR spin cycling room, sauna, steam room, etc.Dishwasher,Elevator,Patio/Deck,Pool,Storage1.00.0USDNoThumbnailNaN2475$2,475Monthly130350 West 50th StManhattanNY40.7629-73.9885Listanza1577289784
65668627426housing/rent/apartmentStudio apartment 2432 Penmar AveThis unit is located at 2432 Penmar Ave, Venice, 90291, CAMonthly rental rates range from $1800We have studio units available for rentNaNNaN0.0USDNoThumbnailNaN1800$1,800Monthly1322432 Penmar AvenueVeniceCA33.9932-118.4609RentLingo1577359461
75668626687housing/rent/apartmentStudio apartment Oak St NW, 16th St NW, Washington, DC 20010This unit is located at Oak St NW, 16th St NW, Washington, DC 20010, Washington, 20010, DCMonthly rental rates range from $840We have studio units available for rentNaNNaN0.0USDNoThumbnailNaN840$840Monthly136NaNWashingtonDC38.9328-77.0297RentLingo1577359393
85668610290housing/rent/apartmentStudio apartment 333 Hyde StThis unit is located at 333 Hyde St, San Francisco, 94109, CAMonthly rental rates range from $1495We have studio units available for rent Apartment features include:-- On Bus Line- RefrigeratorRefrigerator1.00.0USDNoThumbnailNaN1495$1,495Monthly138333 Hyde StSan FranciscoCA37.7599-122.4379RentLingo1577358313
95668627023housing/rent/apartmentStudio apartment A St SE, 19th St SE, WashingtonThis unit is located at A St SE, 19th St SE, Washington, Washington, 20003, DCMonthly rental rates range from $890We have studio units available for rentNaNNaN0.0USDNoThumbnailNaN890$890Monthly141NaNWashingtonDC38.9118-77.0132RentLingo1577359424
idcategorytitlebodyamenitiesbathroomsbedroomscurrencyfeehas_photopets_allowedpriceprice_displayprice_typesquare_feetaddresscitynamestatelatitudelongitudesourcetime
99905659901599housing/rent/apartmentFour BR 864 Teakwood RdThis unit is located at 864 Teakwood Rd, Los Angeles, 90049, CAMonthly rental rates range from $19500We have 4 beds units available for rentDishwasher,Parking,Pool,Refrigerator5.04.0USDNoThumbnailCats,Dogs19500$19,500Monthly5000864 Teakwood RoadLos AngelesCA34.0372-118.2972RentLingo1576666648
99915668642257housing/rent/apartmentSix BR 245 Seawright DrThis unit is located at 245 Seawright Dr, Fayetteville, 30215, GAMonthly rental rates range from $3200We have 6 beds units available for rentNaN5.06.0USDNoThumbnailNaN3200$3,200Monthly5199245 Seawright DriveFayettevilleGA33.4072-84.4523RentLingo1577360489
99925659917503housing/rent/apartmentFour BR 17595 Burl Oak CourtThis unit is located at 17595 Burl Oak Court, Eden Prairie, 55347, MNMonthly rental rates range from $4500We have 4 beds units available for rentNaN5.04.0USDNoThumbnailNaN4500$4,500Monthly540717595 Burl Oak CTEden PrairieMN44.8653-93.4749RentLingo1576667692
99935668627239housing/rent/apartmentFive BR 18605 Avenue MonacoThis unit is located at 18605 Avenue Monaco, Lutz, 33558, FLMonthly rental rates range from $6900We have 5 beds units available for rentDishwasher,Pool,Refrigerator5.05.0USDNoThumbnailCats,Dogs6900$6,900Monthly570018605 AveLutzFL28.1253-82.4468RentLingo1577359442
99945664597657housing/rent/apartmentSix BR 2536 W Canyon Ridge Rd.This unit is located at 2536 W Canyon Ridge Rd., St. George, 84770, UTMonthly rental rates range from $3000We have 6 beds units available for rentNaN4.06.0USDNoThumbnailNaN3000$3,000Monthly59212536 W Canyon Ridge RoadSaint GeorgeUT37.0835-113.5823RentLingo1577017103
99955630240092housing/rent/apartmentFive BR 5407 Abbott Place - AbbottThis unit is located at 5407 Abbott Place - Abbott, Edina, 55410, MNMonthly rental rates range from $6000We have 5 beds units available for rentNaN4.05.0USDNoThumbnailNaN6000$6,000Monthly63005407 Abbott Place  AbbottEdinaMN44.9000-93.3233RentLingo1575112975
99965668640983housing/rent/apartmentSix BR 256 Las EntradasThis unit is located at 256 Las Entradas, Montecito, 93108, CAMonthly rental rates range from $25000We have 6 beds units available for rentNaN8.06.0USDNoThumbnailNaN25000$25,000Monthly8716256 Las EntradasMontecitoCA34.4331-119.6331RentLingo1577360419
99975668643292housing/rent/apartmentSix BR 9908 Bentcross DriveThis unit is located at 9908 Bentcross Drive, Potomac, 20854, MDMonthly rental rates range from $11000We have 6 beds units available for rentNaN8.56.0USDNoThumbnailNaN11000$11,000Monthly113189908 Bentcross DrPotomacMD39.0287-77.2409RentLingo1577360560
99985668662559housing/rent/apartmentOne BR in New York NY 10069Monthly Rent$4,605 -to $4,790AmenitiesThe Aldyn offers some of the finest amenities amongst Upper West Side apartments. The club-style way of apartment living offers residents countless amenities right at their doorstep - the cornerstone being the 40,000 sq. feet La Palestra Athletic Club and Spa. This prestigious facility includes a 75 foot indoor pool, hot bath-tub, 38 foot rock climbing wall, basketball and squash courts, personal training, bowling alley, and more!Community Amenities 75 ft indoor swimming pool and hot bathtub 38 Rock Climbing Wall Elegant Roman and Williams designed lobby & lounge Childrens indoor children's play area by KIDVILLE Pets allowed Free shuttle to Columbus Circle & 72nd/Broadway Entertainment room with gourmet catering kitchen 24hr attendant at door/concierge services On-site parking garage Private landscaped court yard by Mathews Nielson Squash court Bike Room Bowling Alley, game room and golf simulator Spa features mens & peoples locker roomsBasketball,Cable or Satellite,Doorman,Hot Tub,Internet Access,Parking,Playground,Pool,Storage,Washer DryerNaN1.0USDNoThumbnailNaN4790$4,790Monthly40000NaNNew YorkNY40.7716-73.9876Listanza1577362186
99995509132540housing/rent/apartmentBeautiful Lawrenceville Apartment for rentSquare footage: 880 sq. feet, unit number: 15003. Nestled on a secluded hill outside of Atlanta, in Lawrenceville, Georgia, has one, 2 and 3 beds apartment homes conveniently located just off I-85, Duluth, Georgia Highways 316 and 120. Our central location makes for easy commuting to work or leisure-time destinations. Holland Park is near 2 AMC movie theaters and an IMAX theatre and just a few minutes from the Mall of Georgia, Gwinnett Mall and Discover Mills Mall. Families with children appreciate our location in the award-winning Collin's Hill High School cluster. Living in your new apartment Park Apartments, you'll be close to Lawrenceville's quaint town with boutiques, independently owned restaurants and bars, plus terrific night life and community events. On our premises, available amenities a free 24-hr state-of-the-art Fitness facilities. Enjoy a refreshing swim in 1 of our two, resort-style pools with patio areas, grills and shady arbors. Our four-legged residents will love our Bark Park, too.Gym,Patio/Deck1.01.0USDNoNoNaN1009$1,009Monthly880NaNLawrencevilleGA34.0072-84.0034RentDigs.com1577362141